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Found 4 papers, 3 papers with code
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
We develop a principled approach to end-to-end learning in stochastic optimization.
Metrizing Fairness
This means that the distributions of the predictions within the two groups should be close with respect to the Kolmogorov distance, and fairness is achieved by penalizing the dissimilarity of these two distributions in the objective function of the learning problem.
QUACKIE: A NLP Classification Task With Ground Truth Explanations
NLP Interpretability aims to increase trust in model predictions.
On the Granularity of Explanations in Model Agnostic NLP Interpretability
Current methods for Black-Box NLP interpretability, like LIME or SHAP, are based on altering the text to interpret by removing words and modeling the Black-Box response.
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